Automatic Exploratory Data Analysis Report

AutoML Leaderboard

Best model	name	model_type	metric_type	metric_value	train_time
	1_Baseline	Baseline	logloss	0.693141	1.76
	2_DecisionTree	Decision Tree	logloss	0.726109	17.04
	3_Linear	Linear	logloss	0.666576	6.75
	4_Default_Xgboost	Xgboost	logloss	0.667947	6.27
	5_Default_NeuralNetwork	Neural Network	logloss	0.912809	3.86
	6_Default_RandomForest	Random Forest	logloss	0.666313	8.22
the best	Ensemble	Ensemble	logloss	0.638066	0.57

AutoML Performance

AutoML Performance Boxplot

Features Importance

Spearman Correlation of Models

Summary of 1_Baseline

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Baseline Classifier (Baseline)

• n_jobs: -1
• explain_level: 2

Validation

• validation_type: split
• train_ratio: 0.75
• shuffle: True
• stratify: True

Optimized metric

logloss

Training time

1.1 seconds

Metric details

	score	threshold
logloss	0.693141	nan
auc	0.5	nan
f1	0.665049	0.447818
accuracy	0.498182	0.447818
precision	0.498182	0.447818
recall	1	0.447818
mcc	0	0.447818

Confusion matrix (at threshold=0.447818)

	Predicted as 0	Predicted as 1
Labeled as 0	0	276
Labeled as 1	0	274

Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 2_DecisionTree

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Decision Tree

• n_jobs: -1
• criterion: gini
• max_depth: 3
• explain_level: 2

Validation

• validation_type: split
• train_ratio: 0.75
• shuffle: True
• stratify: True

Optimized metric

logloss

Training time

16.0 seconds

Metric details

	score	threshold
logloss	0.726109	nan
auc	0.596953	nan
f1	0.671623	0.228324
accuracy	0.574545	0.404229
precision	0.694444	0.641564
recall	1	0
mcc	0.167123	0.404229

Confusion matrix (at threshold=0.404229)

	Predicted as 0	Predicted as 1
Labeled as 0	99	177
Labeled as 1	57	217

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

SHAP Dependence plots

Dependence (Fold 1)

SHAP Decision plots

Top-10 Worst decisions for class 0 (Fold 1)

Top-10 Best decisions for class 0 (Fold 1)

Top-10 Worst decisions for class 1 (Fold 1)

Top-10 Best decisions for class 1 (Fold 1)

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Summary of 3_Linear

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Logistic Regression (Linear)

• n_jobs: -1
• explain_level: 2

Validation

• validation_type: split
• train_ratio: 0.75
• shuffle: True
• stratify: True

Optimized metric

logloss

Training time

5.9 seconds

Metric details

	score	threshold
logloss	0.666576	nan
auc	0.676465	nan
f1	0.678996	0.219655
accuracy	0.647273	0.57665
precision	0.823529	0.884988
recall	1	0.00341109
mcc	0.309282	0.57665

Confusion matrix (at threshold=0.57665)

	Predicted as 0	Predicted as 1
Labeled as 0	222	54
Labeled as 1	140	134

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

SHAP Dependence plots

Dependence (Fold 1)

SHAP Decision plots

Top-10 Worst decisions for class 0 (Fold 1)

Top-10 Best decisions for class 0 (Fold 1)

Top-10 Worst decisions for class 1 (Fold 1)

Top-10 Best decisions for class 1 (Fold 1)

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Summary of 4_Default_Xgboost

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Extreme Gradient Boosting (Xgboost)

• n_jobs: -1
• objective: binary:logistic
• eta: 0.075
• max_depth: 6
• min_child_weight: 1
• subsample: 1.0
• colsample_bytree: 1.0
• eval_metric: logloss
• explain_level: 2

Validation

• validation_type: split
• train_ratio: 0.75
• shuffle: True
• stratify: True

Optimized metric

logloss

Training time

5.5 seconds

Metric details

	score	threshold
logloss	0.667947	nan
auc	0.631612	nan
f1	0.678055	0.361823
accuracy	0.603636	0.503454
precision	0.680851	0.594797
recall	1	0.192084
mcc	0.212368	0.537786

Confusion matrix (at threshold=0.503454)

	Predicted as 0	Predicted as 1
Labeled as 0	182	94
Labeled as 1	124	150

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 5_Default_NeuralNetwork

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Neural Network

• n_jobs: -1
• dense_1_size: 32
• dense_2_size: 16
• learning_rate: 0.05
• explain_level: 2

Validation

• validation_type: split
• train_ratio: 0.75
• shuffle: True
• stratify: True

Optimized metric

logloss

Training time

3.1 seconds

Metric details

	score	threshold
logloss	0.912809	nan
auc	0.659936	nan
f1	0.673025	0.126637
accuracy	0.630909	0.532048
precision	0.712871	0.790249
recall	1	3.82163e-08
mcc	0.274448	0.664782

Confusion matrix (at threshold=0.532048)

	Predicted as 0	Predicted as 1
Labeled as 0	177	99
Labeled as 1	104	170

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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Summary of 6_Default_RandomForest

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Random Forest

• n_jobs: -1
• criterion: gini
• max_features: 0.9
• min_samples_split: 30
• max_depth: 4
• eval_metric_name: logloss
• explain_level: 2

Validation

• validation_type: split
• train_ratio: 0.75
• shuffle: True
• stratify: True

Optimized metric

logloss

Training time

7.3 seconds

Metric details

	score	threshold
logloss	0.666313	nan
auc	0.642957	nan
f1	0.676056	0.317456
accuracy	0.625455	0.489019
precision	0.646465	0.526
recall	1	0.169989
mcc	0.254651	0.470222

Confusion matrix (at threshold=0.489019)

	Predicted as 0	Predicted as 1
Labeled as 0	164	112
Labeled as 1	94	180

Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

SHAP Dependence plots

Dependence (Fold 1)

SHAP Decision plots

Top-10 Worst decisions for class 0 (Fold 1)

Top-10 Best decisions for class 0 (Fold 1)

Top-10 Worst decisions for class 1 (Fold 1)

Top-10 Best decisions for class 1 (Fold 1)

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Exploratory Data Analysis

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Feature : target

• Feature type : discrete
• Missing : 0.0%
• Unique : 2
• Count :2200.0
• Mean :0.49772727272727274
• Std :0.5001085086277249
• Min :0.0
• 25%th Percentile : 0.0
• 50%th Percentile : 0.0
• 75%th Percentile : 1.0
• Max :1.0

Feature : I

• Feature type : continous
• Missing : 0.0%
• Unique : 33
• Count :2200.0
• Mean :5.192272727272727
• Std :4.826864560026363
• Min :0.0
• 25%th Percentile : 2.0
• 50%th Percentile : 4.0
• 75%th Percentile : 7.0
• Max :47.0

Feature :

• Feature type : continous
• Missing : 0.0%
• Unique : 558
• Count :2200.0
• Mean :227.02772727272728
• Std :165.8213990536209
• Min :21.0
• 25%th Percentile : 125.75
• 50%th Percentile : 171.0
• 75%th Percentile : 275.0
• Max :1056.0

Feature : t

• Feature type : continous
• Missing : 0.0%
• Unique : 300
• Count :2200.0
• Mean :89.90272727272728
• Std :67.28472884409683
• Min :3.0
• 25%th Percentile : 49.0
• 50%th Percentile : 68.0
• 75%th Percentile : 107.0
• Max :459.0

Feature : h

• Feature type : continous
• Missing : 0.0%
• Unique : 203
• Count :2200.0
• Mean :53.13
• Std :41.24111832669844
• Min :1.0
• 25%th Percentile : 28.0
• 50%th Percentile : 40.0
• 75%th Percentile : 64.0
• Max :335.0

Feature : o

• Feature type : continous
• Missing : 0.0%
• Unique : 248
• Count :2200.0
• Mean :74.22227272727272
• Std :55.18795990050051
• Min :6.0
• 25%th Percentile : 40.0
• 50%th Percentile : 56.0
• 75%th Percentile : 89.0
• Max :379.0

Feature : u

• Feature type : continous
• Missing : 0.0%
• Unique : 120
• Count :2200.0
• Mean :26.71090909090909
• Std :20.635538229205746
• Min :0.0
• 25%th Percentile : 14.0
• 50%th Percentile : 20.0
• 75%th Percentile : 33.0
• Max :155.0

Feature : g

• Feature type : continous
• Missing : 0.0%
• Unique : 96
• Count :2200.0
• Mean :20.044545454545453
• Std :16.01198440643309
• Min :0.0
• 25%th Percentile : 10.0
• 50%th Percentile : 15.0
• 75%th Percentile : 25.0
• Max :123.0

Feature : i

• Feature type : continous
• Missing : 0.0%
• Unique : 250
• Count :2200.0
• Mean :71.86318181818181
• Std :54.4215595808837
• Min :6.0
• 25%th Percentile : 38.0
• 50%th Percentile : 54.0
• 75%th Percentile : 87.0
• Max :408.0

Feature : s

• Feature type : continous
• Missing : 0.0%
• Unique : 240
• Count :2200.0
• Mean :66.11363636363636
• Std :50.53181290949742
• Min :7.0
• 25%th Percentile : 34.0
• 50%th Percentile : 50.0
• 75%th Percentile : 79.0
• Max :368.0

Feature : w

• Feature type : continous
• Missing : 0.0%
• Unique : 86
• Count :2200.0
• Mean :18.352272727272727
• Std :13.986968349144975
• Min :0.0
• 25%th Percentile : 9.0
• 50%th Percentile : 14.0
• 75%th Percentile : 23.0
• Max :106.0

Feature : a

• Feature type : continous
• Missing : 0.0%
• Unique : 270
• Count :2200.0
• Mean :78.34681818181818
• Std :59.95500199009446
• Min :4.0
• 25%th Percentile : 41.0
• 50%th Percentile : 59.0
• 75%th Percentile : 95.0
• Max :420.0

Feature : n

• Feature type : continous
• Missing : 0.0%
• Unique : 235
• Count :2200.0
• Mean :65.1
• Std :50.60715492052079
• Min :0.0
• 25%th Percentile : 33.75
• 50%th Percentile : 49.0
• 75%th Percentile : 80.0
• Max :384.0

Feature : d

• Feature type : continous
• Missing : 0.0%
• Unique : 141
• Count :2200.0
• Mean :33.90409090909091
• Std :25.979709814530494
• Min :1.0
• 25%th Percentile : 18.0
• 50%th Percentile : 26.0
• 75%th Percentile : 41.0
• Max :174.0

Feature : e

• Feature type : continous
• Missing : 0.0%
• Unique : 366
• Count :2200.0
• Mean :118.57909090909091
• Std :90.15774725338514
• Min :8.0
• 25%th Percentile : 62.0
• 50%th Percentile : 89.0
• 75%th Percentile : 145.0
• Max :590.0

Feature : r

• Feature type : continous
• Missing : 0.0%
• Unique : 231
• Count :2200.0
• Mean :59.266363636363636
• Std :47.274415439221436
• Min :1.0
• 25%th Percentile : 29.0
• 50%th Percentile : 44.0
• 75%th Percentile : 71.0
• Max :329.0

Feature : f

• Feature type : continous
• Missing : 0.0%
• Unique : 105
• Count :2200.0
• Mean :21.513636363636362
• Std :17.63437451623792
• Min :0.0
• 25%th Percentile : 10.0
• 50%th Percentile : 16.0
• 75%th Percentile : 27.0
• Max :157.0

Feature : l

• Feature type : continous
• Missing : 0.0%
• Unique : 173
• Count :2200.0
• Mean :43.16045454545455
• Std :33.2524372594712
• Min :2.0
• 25%th Percentile : 22.0
• 50%th Percentile : 32.5
• 75%th Percentile : 52.0
• Max :225.0

Feature : y

• Feature type : continous
• Missing : 0.0%
• Unique : 85
• Count :2200.0
• Mean :20.331818181818182
• Std :15.364182533533034
• Min :1.0
• 25%th Percentile : 10.0
• 50%th Percentile : 16.0
• 75%th Percentile : 25.0
• Max :117.0

Feature : p

• Feature type : continous
• Missing : 0.0%
• Unique : 87
• Count :2200.0
• Mean :16.353636363636365
• Std :13.793792075503905
• Min :0.0
• 25%th Percentile : 8.0
• 50%th Percentile : 12.0
• 75%th Percentile : 20.0
• Max :110.0

Feature : m

• Feature type : continous
• Missing : 0.0%
• Unique : 115
• Count :2200.0
• Mean :25.89409090909091
• Std :19.850261689034596
• Min :1.0
• 25%th Percentile : 13.0
• 50%th Percentile : 20.0
• 75%th Percentile : 32.0
• Max :153.0

Feature : k

• Feature type : continous
• Missing : 0.0%
• Unique : 49
• Count :2200.0
• Mean :8.186818181818182
• Std :7.144077508064534
• Min :0.0
• 25%th Percentile : 3.0
• 50%th Percentile : 6.0
• 75%th Percentile : 11.0
• Max :58.0

Feature : ,

• Feature type : continous
• Missing : 0.0%
• Unique : 64
• Count :2200.0
• Mean :10.64090909090909
• Std :10.292058824352731
• Min :0.0
• 25%th Percentile : 4.0
• 50%th Percentile : 8.0
• 75%th Percentile : 14.0
• Max :90.0

Feature : c

• Feature type : continous
• Missing : 0.0%
• Unique : 122
• Count :2200.0
• Mean :26.227272727272727
• Std :21.395285396910815
• Min :0.0
• 25%th Percentile : 13.0
• 50%th Percentile : 20.0
• 75%th Percentile : 32.0
• Max :181.0

Feature : -

• Feature type : continous
• Missing : 0.0%
• Unique : 31
• Count :2200.0
• Mean :2.707272727272727
• Std :4.572267652422172
• Min :0.0
• 25%th Percentile : 0.0
• 50%th Percentile : 1.0
• 75%th Percentile : 4.0
• Max :88.0

Feature : .

• Feature type : continous
• Missing : 0.0%
• Unique : 64
• Count :2200.0
• Mean :12.896818181818182
• Std :9.752917911510567
• Min :0.0
• 25%th Percentile : 7.0
• 50%th Percentile : 10.0
• 75%th Percentile : 16.0
• Max :93.0

Feature : T

• Feature type : continous
• Missing : 0.0%
• Unique : 31
• Count :2200.0
• Mean :4.361363636363636
• Std :4.041379639457143
• Min :0.0
• 25%th Percentile : 2.0
• 50%th Percentile : 3.0
• 75%th Percentile : 6.0
• Max :45.0

Feature : b

• Feature type : continous
• Missing : 0.0%
• Unique : 91
• Count :2200.0
• Mean :18.351363636363637
• Std :14.964675640048645
• Min :0.0
• 25%th Percentile : 8.0
• 50%th Percentile : 14.0
• 75%th Percentile : 23.0
• Max :121.0

Feature : (

• Feature type : continous
• Missing : 0.0%
• Unique : 19
• Count :2200.0
• Mean :1.3595454545454546
• Std :2.146159947862112
• Min :0.0
• 25%th Percentile : 0.0
• 50%th Percentile : 1.0
• 75%th Percentile : 2.0
• Max :25.0


Feature : v

• Feature type : continous
• Missing : 0.0%
• Unique : 59
• Count :2200.0
• Mean :12.118636363636364
• Std :8.907488659021192
• Min :0.0
• 25%th Percentile : 6.0
• 50%th Percentile : 10.0
• 75%th Percentile : 15.0
• Max :75.0

Feature : )

• Feature type : continous
• Missing : 0.0%
• Unique : 19
• Count :2200.0
• Mean :1.4036363636363636
• Std :2.250922796879753
• Min :0.0
• 25%th Percentile : 0.0
• 50%th Percentile : 1.0
• 75%th Percentile : 2.0
• Max :25.0

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Feature : W

• Feature type : continous
• Missing : 0.0%
• Unique : 19
• Count :2200.0
• Mean :1.3681818181818182
• Std :1.9745737939363577
• Min :0.0
• 25%th Percentile : 0.0
• 50%th Percentile : 1.0
• 75%th Percentile : 2.0
• Max :26.0

Feature : z

• Feature type : continous
• Missing : 0.0%
• Unique : 15
• Count :2200.0
• Mean :0.8345454545454546
• Std :1.5201802365338954
• Min :0.0
• 25%th Percentile : 0.0
• 50%th Percentile : 0.0
• 75%th Percentile : 1.0
• Max :14.0

Feature : M

• Feature type : continous
• Missing : 0.0%
• Unique : 22
• Count :2200.0
• Mean :1.66
• Std :2.5678319260348594
• Min :0.0
• 25%th Percentile : 0.0
• 50%th Percentile : 1.0
• 75%th Percentile : 2.0
• Max :24.0

Feature : P

• Feature type : continous
• Missing : 0.0%
• Unique : 19
• Count :2200.0
• Mean :0.9731818181818181
• Std :1.7189283715071715
• Min :0.0
• 25%th Percentile : 0.0
• 50%th Percentile : 0.0
• 75%th Percentile : 1.0
• Max :19.0

Feature : 2

• Feature type : continous
• Missing : 0.0%
• Unique : 8
• Count :2200.0
• Mean :0.3145454545454546
• Std :0.7498171811302042
• Min :0.0
• 25%th Percentile : 0.0
• 50%th Percentile : 0.0
• 75%th Percentile : 0.0
• Max :7.0

Feature : :

• Feature type : continous
• Missing : 0.0%
• Unique : 12
• Count :2200.0
• Mean :0.37545454545454543
• Std :1.0137469265250085
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• 25%th Percentile : 0.0
• 50%th Percentile : 0.0
• 75%th Percentile : 0.0
• Max :18.0

Feature : R

• Feature type : continous
• Missing : 0.0%
• Unique : 20
• Count :2200.0
• Mean :1.1286363636363637
• Std :2.021777288842736
• Min :0.0
• 25%th Percentile : 0.0
• 50%th Percentile : 0.0
• 75%th Percentile : 1.0
• Max :26.0

Feature : A

• Feature type : continous
• Missing : 0.0%
• Unique : 26
• Count :2200.0
• Mean :2.5322727272727272
• Std :3.2811080657789202
• Min :0.0
• 25%th Percentile : 1.0
• 50%th Percentile : 2.0
• 75%th Percentile : 3.0
• Max :46.0

Feature : <

• Feature type : continous
• Missing : 0.0%
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• Count :2200.0
• Mean :4.0
• Std :5.396847815932941
• Min :0.0
• 25%th Percentile : 0.0
• 50%th Percentile : 2.0
• 75%th Percentile : 6.0
• Max :86.0

Feature : /

• Feature type : continous
• Missing : 0.0%
• Unique : 37
• Count :2200.0
• Mean :4.26
• Std :5.573408573637372
• Min :0.0
• 25%th Percentile : 0.0
• 50%th Percentile : 2.0
• 75%th Percentile : 6.0
• Max :86.0

Feature : >

• Feature type : continous
• Missing : 0.0%
• Unique : 26
• Count :2200.0
• Mean :4.001363636363636
• Std :5.397142554455104
• Min :0.0
• 25%th Percentile : 0.0
• 50%th Percentile : 2.0
• 75%th Percentile : 6.0
• Max :86.0

Feature : '

• Feature type : continous
• Missing : 0.0%
• Unique : 34
• Count :2200.0
• Mean :4.954545454545454
• Std :5.064003926162623
• Min :0.0
• 25%th Percentile : 1.0
• 50%th Percentile : 3.0
• 75%th Percentile : 7.0
• Max :55.0

Feature : ?

• Feature type : continous
• Missing : 0.0%
• Unique : 13
• Count :2200.0
• Mean :0.6068181818181818
• Std :1.3318940151904668
• Min :0.0
• 25%th Percentile : 0.0
• 50%th Percentile : 0.0
• 75%th Percentile : 1.0
• Max :14.0

Feature : S

• Feature type : continous
• Missing : 0.0%
• Unique : 26
• Count :2200.0
• Mean :2.4813636363636364
• Std :3.318971053445825
• Min :0.0
• 25%th Percentile : 0.0
• 50%th Percentile : 1.0
• 75%th Percentile : 3.0
• Max :40.0

Feature : J

• Feature type : continous
• Missing : 0.0%
• Unique : 16
• Count :2200.0
• Mean :0.7522727272727273
• Std :1.503972099662669
• Min :0.0
• 25%th Percentile : 0.0
• 50%th Percentile : 0.0
• 75%th Percentile : 1.0
• Max :19.0

Feature : "

• Feature type : continous
• Missing : 0.0%
• Unique : 26
• Count :2200.0
• Mean :2.629090909090909
• Std :4.3820159456561845
• Min :0.0
• 25%th Percentile : 0.0
• 50%th Percentile : 0.0
• 75%th Percentile : 4.0
• Max :36.0

Feature : x

• Feature type : continous
• Missing : 0.0%
• Unique : 19
• Count :2200.0
• Mean :1.6995454545454545
• Std :2.0974875662807633
• Min :0.0
• 25%th Percentile : 0.0
• 50%th Percentile : 1.0
• 75%th Percentile : 2.0
• Max :20.0

Feature : j

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• 50%th Percentile : 1.0
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Feature : D

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• 50%th Percentile : 1.0
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Feature : 1

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Feature : 5

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• 50%th Percentile : 0.0
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Feature : L

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• Mean :1.0745454545454545
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Feature : B

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• Mean :1.6981818181818182
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Feature : V

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• Mean :0.5831818181818181
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Feature : G

• Feature type : continous
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• Count :2200.0
• Mean :0.9277272727272727
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Feature : Y

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• Mean :0.4618181818181818
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Feature : O

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• Count :2200.0
• Mean :1.0990909090909091
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Feature : 0

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• Mean :0.7790909090909091
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Feature : q

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• Mean :0.7040909090909091
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Feature : &

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• Mean :0.1968181818181818
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Feature : 7

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• Mean :0.17454545454545456
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Feature : 8

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• Mean :0.19227272727272726
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Feature : 9

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• Mean :0.4290909090909091
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Feature : H

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• Missing : 0.0%
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• Count :2200.0
• Mean :1.56
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Feature : E

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• Mean :1.229090909090909
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Feature : +

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Feature : K

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• Mean :0.5713636363636364
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Feature : F

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• Mean :0.9340909090909091
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Feature : C

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• Mean :1.4831818181818182
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Feature : U

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• Mean :0.3563636363636364
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Feature : !

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Feature : *

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Feature : N

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Feature : ;

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Feature : 4

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Feature : à

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Feature : è

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Feature : $

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Feature : 3

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Feature : %

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Feature : Z

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Feature : é

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Feature : X

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Feature : 6

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Feature : Q

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• Count :2200.0
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Feature : û

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Feature : `

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Feature : ü

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• Count :2200.0
• Mean :0.00045454545454545455
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Summary of Ensemble

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Ensemble structure

Model	Weight
3_Linear	2
5_Default_NeuralNetwork	1
6_Default_RandomForest	2

Metric details

	score	threshold
logloss	0.638066	nan
auc	0.691804	nan
f1	0.680908	0.304829
accuracy	0.667273	0.56309
precision	0.807692	0.738793
recall	1	0.0699329
mcc	0.350322	0.569713

Confusion matrix (at threshold=0.56309)

	Predicted as 0	Predicted as 1
Labeled as 0	225	51
Labeled as 1	132	142

Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

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